Ibm Watson Quotes

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IBM experimented with adding Urban Dictionary data to its artificial intelligence system Watson, only to scrub it all out again when the computer started swearing at them.
Gretchen McCulloch (Because Internet: Understanding the New Rules of Language)
(Thomas J. Watson Sr. of IBM followed the same rule: “I’m no genius,” he said. “I’m smart in spots—but I stay around those spots.”)
Warren Buffett (The Essays of Warren Buffett: Lessons for Corporate America)
A while back, I came across a line attributed to IBM founder Thomas Watson. If you want to achieve excellence, he said, you can get there today. As of this second, quit doing less-than-excellent work.
Tom Peters
Goods in any storehouse are useless until somebody takes them out and puts them to the use they were meant for. That applies to what man stores away in his brain, too. —THOMAS J. WATSON, FORMER PRESIDENT OF IBM
Josh Kaufman (The Personal MBA: A World-Class Business Education in a Single Volume)
Watson, Sr., was running IBM, he decided they would never have more than four layers from the chairman of the board to the lowest level in the company. That may have been one of the greatest single reasons why IBM was successful.
Sam Walton (Sam Walton: Made In America)
… as I associated with more and more different types, I realized that to make it, you had to get along with almost everybody. If you dislike the people you work with, you’d better not show it. I learned that to be a good leader, I had to strike a delicate balance.
Thomas J. Watson Jr. (Father, Son & Co.: My Life at IBM and Beyond)
A promising junior executive of IBM was involved in a risky venture for the company and managed to lose over $10 million in the gamble. It was a disaster. When Watson called the nervous executive into his office, the young man blurted out, 'I guess you want my resignation?' Watson said, 'You can't be serious. We've just spent $10 million educating you!
Warren Bennis (Leaders: The Strategies for Taking Charge (Collins Business Essentials))
And Thomas Watson, chairman of IBM, said in 1943, “I think there is a world market for maybe five computers.
Michio Kaku (Physics Of The Future: How Science Will Shape Human Destiny And Our Daily Lives By The Year 2100)
certain group of people in the United States tried an experiment. They tried the experiment of making a fortune without working, of making a fortune through the stock exchange. They extended the experiment until it exploded and all went down to earth.  “Aspects of World Trade” Thomas J. Watson Sr. July 31, 1930
Peter Greulich (The World's Greatest Salesman, An IBM Caretaker's Perspective: Looking Back)
Big Thinkers like IBM’s Watson are programmed and named to sound like men.
Jaclyn Friedman (Unscrewed: Women, Sex, Power, and How to Stop Letting the System Screw Us All)
Every time a seismic shift takes place in our economy, there are people who feel the vibrations long before the rest of us do, vibrations so strong they demand action—action that can seem rash, even stupid. Ferry owner Cornelius Vanderbilt jumped ship when he saw the railroads coming. Thomas Watson Jr., overwhelmed by his sense that computers would be everywhere even when they were nowhere, bet his father’s office-machine company on it: IBM. Jeffrey Preston Bezos had that same experience when he first peered into the maze of connected computers called the World Wide Web and realized that the future of retailing was glowing back at him.
Gary Vaynerchuk (The Thank You Economy (Enhanced Edition))
Under Armour. "Ahora estamos en el punto donde está ocurriendo un cambio y los consumidores están demandando más de esta información. Esta asociación con IBM nos permitirá aportar valor al consumidor de manera inédita, ya que integramos la tecnología de aprendizaje de máquinas de IBM Watson con los robustos datos de la comunidad Connected Fitness de Under Armour, la comunidad digital más grande del mundo de más de 160 millones de miembros". [4]
Club-BPM España y Latinoamérica (El Libro del BPM y la Transformación Digital: Gestión, Automatización e Inteligencia de Procesos (BPM) (BPM - Business Process Management nº 1))
Deep Blue, IBM’s chess-playing computer, was a sole entity, and not a team of self-improving ASIs, but the feeling of going up against it is instructive. Two grandmasters said the same thing: “It’s like a wall coming at you.” IBM’s Jeopardy! champion, Watson, was a team of AIs—to answer every question it performed this AI force multiplier trick, conducting searches in parallel before assigning a probability to each answer.
James Barrat (Our Final Invention: Artificial Intelligence and the End of the Human Era)
The proactive approach to a mistake is to acknowledge it instantly, correct and learn from it. This literally turns a failure into a success. “Success,” said IBM founder T. J. Watson, “is on the far side of failure.” But not to acknowledge a mistake, not to correct it and learn from it, is a mistake of a different order. It usually puts a person on a self-deceiving, self-justifying path, often involving rationalization (rational lies) to
Stephen R. Covey (The 7 Habits of Highly Effective People: Powerful Lessons in Personal Change)
a harbinger of a third wave of computing, one that blurred the line between augmented human intelligence and artificial intelligence. “The first generation of computers were machines that counted and tabulated,” Rometty says, harking back to IBM’s roots in Herman Hollerith’s punch-card tabulators used for the 1890 census. “The second generation involved programmable machines that used the von Neumann architecture. You had to tell them what to do.” Beginning with Ada Lovelace, people wrote algorithms that instructed these computers, step by step, how to perform tasks. “Because of the proliferation of data,” Rometty adds, “there is no choice but to have a third generation, which are systems that are not programmed, they learn.”27 But even as this occurs, the process could remain one of partnership and symbiosis with humans rather than one designed to relegate humans to the dustbin of history. Larry Norton, a breast cancer specialist at New York’s Memorial Sloan-Kettering Cancer Center, was part of the team that worked with Watson. “Computer science is going to evolve rapidly, and medicine will evolve with it,” he said. “This is coevolution. We’ll help each other.”28 This belief that machines and humans will get smarter together is a process that Doug Engelbart called “bootstrapping” and “coevolution.”29 It raises an interesting prospect: perhaps no matter how fast computers progress, artificial intelligence may never outstrip the intelligence of the human-machine partnership. Let us assume, for example, that a machine someday exhibits all of the mental capabilities of a human: giving the outward appearance of recognizing patterns, perceiving emotions, appreciating beauty, creating art, having desires, forming moral values, and pursuing goals. Such a machine might be able to pass a Turing Test. It might even pass what we could call the Ada Test, which is that it could appear to “originate” its own thoughts that go beyond what we humans program it to do. There would, however, be still another hurdle before we could say that artificial intelligence has triumphed over augmented intelligence. We can call it the Licklider Test. It would go beyond asking whether a machine could replicate all the components of human intelligence to ask whether the machine accomplishes these tasks better when whirring away completely on its own or when working in conjunction with humans. In other words, is it possible that humans and machines working in partnership will be indefinitely more powerful than an artificial intelligence machine working alone?
Walter Isaacson (The Innovators: How a Group of Inventors, Hackers, Geniuses, and Geeks Created the Digital Revolution)
I once heard a story about Tom Watson, the founder of IBM. Asked to what he attributed the phenomenal success of IBM, he is said to have answered: IBM is what it is today for three special reasons. The first reason is that, at the very beginning, I had a very clear picture of what the company would look like when it was finally done. You might say I had a model in my mind of what it would look like when the dream—my vision—was in place. The second reason was that once I had that picture, I then asked myself how a company which looked like that would have to act. I then created a picture of how IBM would act when it was finally done. The third reason IBM has been so successful was that once I had a picture of how IBM would look when the dream was in place and how such a company would have to act, I then realized that, unless we began to act that way from the very beginning, we would never get there. In other words, I realized that for IBM to become a great company it would have to act like a great company long before it ever became one. From the very outset, IBM was fashioned after the template of my vision. And each and every day we attempted to model the company after that template. At the end of each day, we asked ourselves how well we did, discovered the disparity between where we were and where we had committed ourselves to be, and, at the start of the following day, set out to make up for the difference. Every day at IBM was a day devoted to business development, not doing business. We didn’t do business at IBM, we built one Now,
Michael E. Gerber (The E-Myth Revisited: Why Most Small Businesses Don't Work and What to Do About It)
As a part of their effort to turn Watson into a practical tool, IBM researchers confronted one of the primary tenets of the big data revolution: the idea that prediction based on correlation is sufficient, and that a deep understanding of causation is usually both unachievable and unnecessary. A new feature they named “WatsonPaths” goes beyond simply providing an answer and lets researchers see the specific sources Watson consulted, the logic it used in its evaluation, and the inferences it made on
Martin Ford (Rise of the Robots: Technology and the Threat of a Jobless Future)
As an IBM document describing the Watson technology points out: “We have noses that run, and feet that smell. How can a slim chance and a fat chance be the same, but a wise man and a wise guy are opposites?
Martin Ford (Rise of the Robots: Technology and the Threat of a Jobless Future)
Would you like me to give you a formula for success? It’s quite simple, really. Double your rate of failure. You are thinking of failure as the enemy of success. But it isn’t at all. You can be discouraged by failure, or you can learn from it. So go ahead and make mistakes. Make all you can. Because remember, that’s where you will find success.”   Thomas Watson, former chairman and CEO of IBM
Calvert Cazier (The Resiliency Toolkit: A Busy Parent's Guide to Raising Happy, Confident, Successful Children)
programmer can fully prepare for in advance. It’s impossible for them to upload every single variable. As you observe the market, in real time, you will see those unpredictable moments and you will profit in them. You must be very strategic with every trade you enter. Never forget that in the equally strategic world of chess, Garry Kasparov did win some of his rounds against IBM’s Deep Blue. More recently, even IBM’s Watson got answer after answer wrong when playing on Jeopardy! You must also remember that any one organization’s powerful “black box” is trading against all of the other
AMS Publishing Group (Intelligent Stock Market Trading and Investment: Quick and Easy Guide to Stock Market Investment for Absolute Beginners)
Mandelbrot appended this statement to his entry in Who’s Who: “Science would be ruined if (like sports) it were to put competition above everything else, and if it were to clarify the rules of competition by withdrawing entirely into narrowly defined specialties. The rare scholars who are nomads-by–choice are essential to the intellectual welfare of the settled disciplines.” This nomad-by–choice, who also called himself a pioneer-by–necessity, withdrew from academe when he withdrew from France, accepting the shelter of IBM’s Thomas J. Watson Research Center.
James Gleick (Chaos: Making a New Science)
The proactive approach to a mistake is to acknowledge it instantly, correct and learn from it. This literally turns a failure into a success. “Success,” said IBM founder T. J. Watson, “is on the far side of failure.
Stephen R. Covey (The 7 Habits of Highly Effective People)
The underlying architectures adopted by IBM Watson and AlphaGo have shown super-humans strengths and sub-humans limitations.
Antonio Lieto (Cognitive Design for Artificial Minds)
John E. Kelly III (Smart Machines: IBM's Watson and the Era of Cognitive Computing)
Since we built such sophisticated business machines, people tended to think of IBM as a model of order and logic—a totally streamlined organization in which we developed plans rationally and carried them out with utter precision. I never thought for a minute that was really the case.
Thomas J. Watson Jr. (Father, Son & Co.: My Life at IBM and Beyond)
A mere 20 watts of energy are required to power the 22 billion neurons in a brain that’s roughly the size of a grapefruit. To field a conventional computer with comparable cognitive capacity would require gigawatts of electricity and a machine the size of a football field.
Steve Hamm (Smart Machines: IBM's Watson and the Era of Cognitive Computing)
I think there is a world wide market for maybe five computers. (Thomas Watson, Chairman IBM, 1943)
Briony J. Oates (Researching Information Systems and Computing)
I think there is a world wide market for maybe five computers. (Thomas Watson, Chairman IBM, 1943) This telephone has too many shortcomings to be seriously considered as a means of communication. The device is inherently of no value to us. (Western Union internal memo, 1876) But what is it good for? (Engineer at the Advanced Computing Systems Division of IBM, 1968, commenting on the microchip) There is no reason why anyone would want a computer in their home. (Ken Olson, president, chairman and founder of Digital Equipment Corporation, 1977) Computers in the future may weigh no more than 1.5 tons. (Popular Mechanics, 1949)
Briony J. Oates (Researching Information Systems and Computing)
IBM’s Watson draws on a plethora of clever algorithms, but it would be uncompetitive without computer hardware that is about one hundred times more powerful than Deep Blue, its chess-playing predecessor that beat the human world champion, Garry Kasparov, in a 1997 match.
Erik Brynjolfsson (The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies)
Watson’s eminently sensible direction was: Respect your customer, and dress accordingly. However, as the years went by, customers changed how they dressed at work, and few of the technical buyers in corporations showed up in white and blue. However, Watson’s sensible connection to the customer was forgotten, and the dress code marched on. When I abolished IBM’s dress code in 1995, it got an extraordinary amount of attention in the press. Some thought it was an action of great portent. In fact, it was one of the easiest decisions I made—or, rather, didn’t make; it wasn’t really a “decision.” We didn’t replace one dress code with another. I simply returned to the wisdom of Mr. Watson and decided: Dress according to the circumstances of your day and recognize who you will be with (customers, government leaders, or just your colleagues in the labs).
Louis V. Gerstner Jr. (Who Says Elephants Can't Dance?)
It’s worth noting that IBM’s program, named Watson, had access to 200 million pages of content consuming four terabytes of memory.
Jerry Kaplan (Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence)
Teachers are being replaced by interactive algorithms that can teach students on a far more customized level, such as the ones being developed by companies like Mindojo. Doctors are under attack from the job replacing algorithms, such as IBM’s Jeopardy game show winning Watson computer, which is now being groomed as a medical diagnosis machine. And unlike with spending years training just one doctor at a time, every technical challenge that is beaten while training Watson will ultimately produce an infinite number of well trained doctor “machines.” Algorithms are already being appointed to fill seats on company boards, such as in May 2014 when a Hong Kong venture-capital firm, Deep Knowledge Ventures, appointed the algorithm VITAL to its board. VITAL studies vast amounts of data then gets to vote on whether the firm makes an investment in a specific company or not.
GBF Summary (Summary: Homo Deus by Yuval Noah Harari (Great Books Fast))
IBM pioneer Thomas Watson said, “If you want to succeed, double your failure rate.” I
Leonard Mlodinow (The Drunkard's Walk: How Randomness Rules Our Lives)
It’s difficult to imagine that Artificial Intelligence will take the place of people but many believe that it’s only a short time before computers will outthink us. They already can beat our best chess players and have been able to out calculate us since calculators first came onto the scene. IBM’s Watson is on the cutting edge of Cognitive Computers, being used to out think our physicians but closer to home, for the greatest part; our cars are no longer assembled by people but rather robots. Our automobiles can be considered among our first robots, since they took the place of horses. Just after the turn of the last century when the population in the United States crossed the 100 M mark the number of horses came to 20M. Now we have a population of 325 M but only 9 M horses. You might ask what happened. Well back in 1915 there were 2.4 M cars but this jumped to 3.6 M in just one year. Although horses still out-numbered cars the handwriting was on the wall! You might think that this doesn’t apply to us but why not? The number of robots increase, taking the place of first our workers on the assembly line and then workers in the food industry and this takes us from tractors and combines on the farms to the cooking and serving hamburgers at your favorite burger joint. People are becoming redundant! That’s right we are becoming superfluous! Worldwide only 7 out of 100 people have college degrees and here in the United States only 40% of our working population possesses a sheep skin, although mine is printed on ordinary paper. With education becoming ever more expensive, we as a population are becoming ever more uneducated. A growing problem is that as computers and robots become smarter, as they are, we are no longer needed to be anything more than a consumer and where will the money come from for that? I recently read that this death spiral will run its course within 40 years! Nice statistics that we’re looking at…. Looking at the bright side of things you can now buy an atomically correct, life sized doll, as perhaps a robotic non-complaining, companion for under $120. In time these robotic beings will be able to talk back but hopefully there will be an off switch. As interesting as this sounds it will most likely not be for everyone, however it may appeal to some of our less capable, not to have to actually interface with real live people. The fact is that most people will soon outlive their usefulness! We as a society are being challenged and there will soon be little reason for our being. When machines make machines that can out think us; when we become dumb and superfluous, then what? Are we ready for this transition? It’s scary but If nothing else, it’s something to think about….
Hank Bracker
If you want to increase your success rate, double your failure rate. Thomas J. Watson, Former Chairman and CEO of IBM
Dan Siroker (A/B Testing: The Most Powerful Way to Turn Clicks Into Customers)
It’s been said every institution is nothing more, but the extended shadow of one person. In IBM’s case, that was Thomas J. Watson, Sr.
BusinessNews Publishing (Summary: Who Says Elephants Can't Dance? - Louis Gerstner: Inside IBM's Historic Turnaround)
«Hace poco me preguntaron si iba a despedir a un empleado que había cometido un error que le había costado a la empresa 600.000 dólares. No, respondí, acabo de gastarme 600.000 en formarle.» THOMAS J. WATSON, fundador de IBM
Timothy Ferriss (La semana laboral de 4 horas)
The first eye-opener came in the 1970s, when DARPA, the Pentagon’s research arm, organized the first large-scale speech recognition project. To everyone’s surprise, a simple sequential learner of the type Chomsky derided handily beat a sophisticated knowledge-based system. Learners like it are now used in just about every speech recognizer, including Siri. Fred Jelinek, head of the speech group at IBM, famously quipped that “every time I fire a linguist, the recognizer’s performance goes up.” Stuck in the knowledge-engineering mire, computational linguistics had a near-death experience in the late 1980s. Since then, learning-based methods have swept the field, to the point where it’s hard to find a paper devoid of learning in a computational linguistics conference. Statistical parsers analyze language with accuracy close to that of humans, where hand-coded ones lagged far behind. Machine translation, spelling correction, part-of-speech tagging, word sense disambiguation, question answering, dialogue, summarization: the best systems in these areas all use learning. Watson, the Jeopardy! computer champion, would not have been possible without it.
Pedro Domingos (The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World)
The human brain is a marvel. A mere 20 watts of energy are required to power the 22 billion neurons in a brain that’s roughly the size of a grapefruit. To field a conventional computer with comparable cognitive capacity would require gigawatts of electricity and a machine the size of a football field.
John E. Kelly III (Smart Machines: IBM's Watson and the Era of Cognitive Computing)
When I joined the company," Watson said in a speech years later, "our three divisions were not disorganized-they were unorganized. There were plenty of ideas lying around, but many of them seemed too big for the organization to handle. The directors told me, `You'll have to go out and hire outside brains before you can build up this company.' I told them, `That's not my policy. I like to develop men from the ranks and promote them.' "2 Watson believed that lifelong employees were more likely to live and breathe the company, and remain dedicated to giving their all.
Kevin Maney (The Maverick and His Machine: Thomas Watson, Sr. and the Making of IBM: Thomas Watson, Sr., and the Making of IBM)
the IBM Surveillance Insight for Financial Services dashboard is a “cognitive surveillance engine” that uses the power of Watson (yes, the Jeopardy-playing computer) to take in and piece together unstructured data, such as employee email, as well as structured data, such as trade transactions, to create a thorough surveillance system that can alert flesh-and-blood compliance personnel to potential issues.
Susanne Chishti (The WEALTHTECH Book: The FinTech Handbook for Investors, Entrepreneurs and Finance Visionaries)
Como dijo una vez Thomas Watson de IBM: “Nada ocurre hasta que la venta se complete.
Allan Dib (El Plan de Marketing de 1-Página: Consigue Nuevos Clientes, Gana Más Dinero, Y Destaca Entre La Multitud)
for entrepreneurs interested in building Watson-backed business, Cane was stunned by how easy it was to work with IBM. “They provided so much support and guidance,” he explains, “that we were able to build our entire Watson-powered prototype in two weeks.
Peter H. Diamandis (Bold: How to Go Big, Create Wealth and Impact the World)
IBM Watson is now doing cognitive cooking, inventing unique recipes that combine ingredients and flavors in new ways.
Robin Farmanfarmaian (The Patient as CEO: How Technology Empowers the Healthcare Consumer)
By contrast, the IBM Selective Sequence Electronic Calculator (SSEC), installed in New York in 1948, refused such easy reading. It was called a calculator because in 1948 computers were still people, and the president of IBM, Thomas J. Watson, wanted to reassure the public that his products were not designed to replace them.20 IBM built the machine as a rival to the ENIAC – but both were descendants of von Neumann’s earlier Harvard Mark I machine, which contributed to the Manhattan Project.
James Bridle (New Dark Age: Technology and the End of the Future)
It takes about twelve years for a pharmaceutical firm to research, develop, test, and launch a product. Several firms, including Pfizer, Novartis, and Celgene, are working with IBM Watson to try to identify and bring new drugs to market faster.
Thomas H. Davenport (The AI Advantage: How to Put the Artificial Intelligence Revolution to Work)
Other firms are working on marketing applications of machine learning that increase customer engagement. Macy’s, for example, is working with both IBM’s Watson and Cognitive Scale, an Austin-based AI vendor, to improve personalization and engagement on its website and mobile app.
Thomas H. Davenport (The AI Advantage: How to Put the Artificial Intelligence Revolution to Work)
When I visited Watson and its programmers recently at IBM’s main research facility—where the machine, consisting of a stack of servers, resides alone in a basement, humming quietly and waiting for questions to crunch on—I inquired (directing my queries to the nearby humans, not the machine) whether Watson might ever turn the tables on us and start asking us wickedly complex questions. While that’s not its purpose, its programmers point out something interesting and quite promising: As Watson comes in increasing contact with doctors and medical students currently using the system, the machine is slowly training them to ask more and better questions in order to pull the information they need out of the system. As it trains them to be better questioners, Watson will almost certainly help them to be better doctors.
Warren Berger (A More Beautiful Question: The Power of Inquiry to Spark Breakthrough Ideas)
In the early days of IBM, when a newly promoted executive lost a lot of money on a bet that went wrong, founder Thomas Watson let everyone know he wasn’t going to be fired. “Why would I fire him?” he asked. “We just spent thirty thousand dollars educating him.
Jason Jennings (The Reinventors: How Extraordinary Companies Pursue Radical Continuous Change)
The proactive approach to a mistake is to acknowledge it instantly, correct and learn from it. This literally turns a failure into a success. “Success,” said IBM founder T. J. Watson, “is on the far side of failure.” But not to acknowledge a mistake, not to correct it and learn from it, is a mistake of a different order. It usually puts a person on a self-deceiving, self-justifying path, often involving rationalization (rational lies) to self and to others. This second mistake, this cover-up, empowers the first, giving it disproportionate importance, and causes far deeper injury to self. It is not what others do or even our own mistakes that hurt us the most; it is our response to those things. Chasing after the poisonous snake that bites us will only drive the poison through our entire system. It is far better to take measures immediately to get the poison out. Our response to any mistake affects the quality of the next moment. It is important to immediately admit and correct our mistakes so that they have no power over that next moment and we are empowered again.
Stephen R. Covey (The 7 Habits of Highly Effective People)
Or as the IBM pioneer Thomas Watson said, “If you want to succeed, double your failure rate.
Leonard Mlodinow (The Drunkard's Walk: How Randomness Rules Our Lives)
If I had to do it all over again, I would have encouraged employees to make more mistakes. —Thomas Watson, IBM
Sahar Hashemi (Switched On: You have it in you, you just need to switch it on)
The head of IBM, Thomas Watson Jr., promised all 60,000 employees loans to build fallout shelters—and arranged to sell any employees construction materials at cost. In the preceding years, the government had conducted several large-scale shelter experiments, including one with California inmates who received a day off their sentences for each day they lived in an underground shelter;
Garrett M. Graff (Raven Rock: The Story of the U.S. Government's Secret Plan to Save Itself--While the Rest of Us Die)
Computers already have enough power to outperform people in activities we used to think of as distinctively human. In 1997, IBM’s Deep Blue defeated world chess champion Garry Kasparov. Jeopardy!’s best-ever contestant, Ken Jennings, succumbed to IBM’s Watson in 2011. And Google’s self-driving cars are already on California roads today. Dale Earnhardt Jr. needn’t feel threatened by them, but the Guardian worries (on behalf of the millions of chauffeurs and cabbies in the world) that self-driving cars “could drive the next wave of unemployment.
Peter Thiel (Zero to One: Notes on Startups, or How to Build the Future)
Tom Watson, though, held his team and himself to a different standard. As he explained to his men in 1930 at the outset of the Depression, “No man deserves any special credit for being an average man. It is the men who are striving to be above the average who are the men who build business—they are the men who build nations.” This belief applied to every person, including himself as the person holding the title of the CEO, because a policy was “a policy for the entire organization; not for just one man.” The average, in his eyes, was “the average” because the “above average” carried the rest. He was determined to be one of the great CEOs who would carry the rest.
Peter Greulich (The World's Greatest Salesman, An IBM Caretaker's Perspective: Looking Back)
We must always set the right kind of example all the way along the line as to character and good manners.  Then you can teach the men anything, because they are with you, they will listen to you. They are not trying to show off or be smart. They get right down to business. “Men of Character and Courtesy” Thomas J. Watson Sr. January 23–25, 1933
Peter Greulich (The World's Greatest Salesman, An IBM Caretaker's Perspective: Looking Back)
You are not coming into an organization that has been built. We are just building it. You are not coming into a business that has succeeded. We are merely succeeding a little more each year. “Advice to Young Men Entering Business” Thomas J. Watson Sr. October 29, 1930
Peter Greulich (The World's Greatest Salesman, An IBM Caretaker's Perspective: Looking Back)
En realidad, ya hemos cruzado esta línea en lo que a la medicina se refiere. En el hospital ya no somos individuos. ¿Quién cree el lector que tomará las decisiones más trascendentales sobre su cuerpo y su salud a lo largo de su vida? Es muy probable que muchas de tales decisiones las tomen algoritmos informáticos como el Watson de IBM. Y esto no es necesariamente una mala noticia. Los diabéticos ya llevan sensores que comprueban automáticamente su nivel de azúcar varias veces al día, y les alertan siempre que este cruza un umbral peligroso. En 2014, investigadores de la Universidad de Yale anunciaron la primera prueba exitosa con un «páncreas artificial» controlado por un iPhone. Cincuenta y dos diabéticos participaron en el experimento. Cada paciente tenía un sensor diminuto y una bomba minúscula implantados en el estómago. La bomba estaba conectada a pequeños tubos de insulina y glucagón, dos hormonas que regulan conjuntamente los niveles de azúcar en sangre. El sensor medía constantemente el nivel de azúcar y transmitía los datos a un iPhone. Este contenía una aplicación que analizaba la información y, siempre que era necesario, daba órdenes a la bomba, que inyectaba cantidades determinadas de insulina o de glucagón…, todo ello sin necesidad de intervención humana.[22]
Yuval Noah Harari (Homo Deus: Breve historia del mañana)
Actually, IBM went through a severe identity crisis. It almost missed the computer opportunity. It became capable of growth only through a palace coup which overthrew Thomas J. Watson, Sr., the company’s founder, its chief executive, and for long years the prophet of “data processing.
Peter F. Drucker (Management: Tasks, Responsibilities, Practices)
The proactive approach to a mistake is to acknowledge it instantly, correct and learn from it. This literally turns a failure into a success. “Success,” said IBM founder T. J. Watson,
Stephen R. Covey (The 7 Habits of Highly Effective People)
A good chief executive is essentially a hard-to-automate decision engine, not unlike IBM’s Jeopardy!-playing Watson system. They have built up a hard-won repository of experience and have honed and proved an instinct for their market. They’re then presented inputs throughout the day—in the form of e-mails, meetings, site visits, and the like—that they must process and act on. To ask a CEO to spend four hours thinking deeply about a single problem is a waste of what makes him or her valuable. It’s better to hire three smart subordinates to think deeply about the problem and then bring their solutions to the executive for a final decision.
Cal Newport (Deep Work: Rules for Focused Success in a Distracted World)
Thomas J. Watson, the former chairman of IBM, said, “Nothing so conclusively proves a man’s ability to lead others as what he does from day to day to lead himself.
John C. Maxwell (The Self-Aware Leader: Play to Your Strengths, Unleash Your Team)